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Secure Edge Computing

Release Date: 2021/08/01
ISBN: 9781000427325
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The internet is making our daily life as digital as possible and this new era is called the Internet of Everything (IoE). Edge computing is an emerging data analytics concept that addresses the challenges associated with IoE. More specifically, edge computing facilitates data analysis at the edge of the network instead of interacting with cloud-based servers. Therefore, more and more devices need to be added in remote locations without any substantial monitoring strategy.

This increased connectivity and the devices used for edge computing will create more room for cyber criminals to exploit the system’s vulnerabilities. Ensuring cyber security at the edge should not be an afterthought or a huge challenge. The devices used for edge computing are not designed with traditional IT hardware protocols. There are diverse-use cases in the context of edge computing and Internet of Things (IoT) in remote locations. However, the cyber security configuration and software updates are often overlooked when they are most needed to fight cyber crime and ensure data privacy. Therefore, the threat landscape in the context of edge computing becomes wider and far more challenging.

There is a clear need for collaborative work throughout the entire value chain of the network. In this context, this book addresses the cyber security challenges associated with edge computing, which provides a bigger picture of the concepts, techniques, applications, and open research directions in this area. In addition, the book serves as a single source of reference for acquiring the knowledge on the technology, process and people involved in next generation computing and security. It will be a valuable aid for researchers, higher level students and professionals working in the area.

Table of Contents

  1. Cover
  2. Half Title
  3. Title Page
  4. Copyright Page
  5. Dedication
  6. Table of Contents
  7. Preface
  8. Acknowledgments
  9. Editors
  10. Contributors
  11. Section I
    1. Chapter 1: Secure Fog-Cloud of Things: Architectures, Opportunities and Challenges
    2. 1.1 Introduction
    3. 1.1.1 Chapter Road Map
    4. 1.2 Secure Fog-Cloud of Things
    5. 1.2.1 Environment
    6. 1.2.2 Architecture
    7. 1.3 Threats, Vulnerabilities and Exploits in Fog-Cloud of Things Ecosystems
    8. 1.4 Key Machine Learning Kits for Secure Fog-Cloud of Things Architecture
    9. 1.5 Applications
    10. 1.6 Opportunities and Challenges in Improving Security in Fog-Cloud of Things
    11. 1.6.1 Opportunities
    12. 1.6.2 Challenges
    13. 1.7 Future Trends
    14. 1.8 Conclusion
    15. References
    16. Chapter 2: Collaborative and Integrated Edge Security Architecture
    17. 2.1 Background
    18. 2.2 Edge Security Challenges
    19. 2.3 Perspectives of Edge Security Architecture
    20. 2.4 Emerging Trends and Enablers for Edge Security Architecture
    21. 2.4.1 The Edge Computing Architecture
    22. 2.4.2 Leveraging Fog-Based Security Architecture for Edge Networks
    23. 2.5 Collaborative and Integrated Security Architecture for Edge Computing
    24. 2.5.1 Overview
    25. 2.5.2 Distributed Virtual Firewall (DFWs)
    26. 2.5.3 Distributed Intrusion Detection Systems (IDSs)
    27. 2.6 Conclusion and Future Research
    28. References
    29. Chapter 3: A Systemic IoT–Fog–Cloud Architecture for Big-Data Analytics and Cyber Security Systems: A Review of Fog Computing
    30. 3.1 Introduction
    31. 3.2 Fog Computing Systems
    32. 3.2.1 Description of Fog
    33. 3.2.2 Characteristics of Fog
    34. 3.2.3 Systemic Architecture of IoT–Fog–Cloud
    35. 3.2.4 Applications of IoT, Fog and Cloud Systems
    36. 3.3 Cyber Security Challenges
    37. 3.4 Security Solutions and Future Directions
    38. 3.5 Conclusion
    39. References
    40. Chapter 4: Security and Organizational Strategy: A Cloud and Edge Computing Perspective
    41. 4.1 Introduction
    42. 4.2 Cloud Computing and Cloud-based Computing
    43. 4.3 Business Operations and Management
    44. 4.3.1 Business Process
    45. 4.3.2 Business Continuity
    46. 4.3.3 Risk Management and Disaster Recovery
    47. 4.4 Human and Technological Factors
    48. 4.4.1 Human Factors
    49. 4.4.2 Technological Factors
    50. 4.4.3 Copyright and SLAs
    51. 4.5 Trust
    52. 4.5.1 Intra-organizational Trust
    53. 4.5.2 Inter-organizational Trust
    54. 4.6 Geographic Location
    55. 4.6.1 Regulations and Jurisdictions
    56. 4.6.2 Compliance and Governance
    57. 4.7 Conclusions
    58. References
    59. Chapter 5: An Overview of Cognitive Internet of Things: Cloud and Fog Computing
    60. 5.1 Introduction
    61. 5.2 Background of Fog, Cloud and Edge Computing
    62. 5.2.1 Fog Computing
    63. 5.2.1.1 Benefits of Fog Computing
    64. 5.2.1.2 Disadvantages of Fog Computing
    65. 5.2.2 Cloud Computing
    66. 5.2.2.1 Benefits of Cloud Computing
    67. 5.2.2.2 Disadvantages of Cloud Computing
    68. 5.2.3 Edge Computing
    69. 5.2.3.1 Benefits of Edge Computing
    70. 5.2.3.2 Disadvantages of Edge Computing
    71. 5.3 Literature Review of Existing Works
    72. 5.3.1 Review of Fog Computing
    73. 5.3.2 Review of Cloud Computing
    74. 5.3.3 Review of Edge Computing
    75. 5.4 Network Architecture
    76. 5.4.1 Computation Between Fog and Cloud
    77. 5.4.2 Computation Between Fog and Fog
    78. 5.5 Numerical Results
    79. 5.6 Conclusion
    80. References
    81. Chapter 6: Privacy of Edge Computing and IoT
    82. 6.1 Introduction
    83. 6.2 IoT Ecosystem
    84. 6.3 Privacy Spaces
    85. 6.4 The Technology of Privacy Spaces
    86. 6.4.1 Apple HomeKit
    87. 6.4.2 Google Home
    88. 6.5 Privacy Space Data Flows
    89. 6.6 Remote Access
    90. 6.7 Personal Data Store
    91. 6.8 Privacy-Preserving Techniques
    92. 6.8.1 Anonymization
    93. 6.8.2 k-anonymization
    94. 6.8.3 Unicity
    95. 6.8.4 Differential Privacy
    96. 6.8.5 Privacy-Preserving Data Queries
    97. 6.9 Case Study: Contact Tracking Mobile Applications
    98. 6.10 Conclusions
    99. Notes
    100. References
  12. Section II
    1. Chapter 7: Reducing the Attack Surface of Edge Computing IoT Networks via Hybrid Routing Using Dedicated Nodes
    2. 7.1 Introduction
    3. 7.2 Related Works
    4. 7.3 The Solution
    5. 7.3.1 Inference System of Trusted Time Server
    6. 7.3.2 Security Features
    7. 7.3.3 Synchronization with a Trusted Time Server
    8. 7.3.4 Transit Addresses
    9. 7.4 Test Methodology and Environment
    10. 7.4.1 TTS Server and Data Collection for Inference
    11. 7.4.2 Heterogeneous Network Environment
    12. Simulation Case 1:
    13. Simulation Case 2:
    14. 7.4.3 Graph-based Representation
    15. 7.5 Case Study
    16. 7.6 Conclusion
    17. Notes
    18. References
    19. Chapter 8: Early Identification of Mental Health Disorder Employing Machine Learning-based Secure Edge Analytics: A Real-time Monitoring System
    20. 8.1 Introduction
    21. 8.2 Traditional Methods Implemented in Edge Computing
    22. 8.3 Secure Analytics of Smart Healthcare at the Edge
    23. 8.4 Related Work: Overview of Mobile Applications for Mental Health
    24. 8.4.1 Anxiety Reliever
    25. 8.4.2 Anxiety Coach
    26. 8.4.3 Breath2Relax
    27. 8.4.4 Happify
    28. 8.4.5 Head Space
    29. 8.4.6 Mindshift
    30. 8.4.7 MoodKit
    31. 8.4.8 Panic Relief
    32. 8.4.9 PTSD Coach
    33. 8.5 Methodologies for Automated Real-Time Mood Detection for Assessing Anxiety and Depression Levels in the Edge with Privacy-Preservation Capability
    34. 8.5.1 Data Preparation and Pre-processing
    35. 8.5.1.1 Identifying Optic Flow in Facial Regions
    36. 8.5.2 Pre-processing and Noise Elimination of the Image Data
    37. 8.5.3 Questionnaire Data Description
    38. 8.5.4 Proposed Architecture
    39. 8.5.5 Data Analysis Using AI Techniques
    40. 8.5.6 Privacy Preservation of the Model
    41. 8.5.6.1 Federated Learning
    42. 8.5.7 Model Deployment on Edge Devices
    43. 8.6 Experimental Results
    44. 8.6.1 SqlLite Analysis
    45. 8.6.2 Machine Learning Algorithm Analysis
    46. 8.6.3 Federated Learning Analysis
    47. 8.6.4 Comparative Analysis
    48. 8.7 Conclusion
    49. References
    50. Chapter 9: Harnessing Artificial Intelligence for Secure ECG Analytics at the Edge for Cardiac Arrhythmia Classification
    51. 9.1 Introduction
    52. 9.2 Literature Review
    53. 9.3 Dataset Preparation
    54. 9.4 Methodology
    55. 9.4.1 ECG Pre-processing Phase
    56. 9.4.2 Heartbeat Segmentation Phase
    57. 9.4.3 Feature Extraction Phase
    58. 9.4.4 Learning/Classification Phase
    59. 9.5 Experimental Setups, Results and Discussion
    60. 9.5.1 Performance Indicators
    61. 9.5.2 Results for Experimental Setup 1
    62. 9.5.3 Results for Experimental Setup 2
    63. 9.6 Conclusion
    64. References
    65. Chapter 10: On Securing Electronic Healthcare Records Using Hyperledger Fabric Across the Network Edge
    66. 10.1 Introduction
    67. 10.2 Existing Decentralized Security Methods: Can Blockchain Be Used At the Edge?
    68. 10.2.1 Current EHR System in Canada
    69. 10.2.2 Challenges with the Traditional EHR Systems
    70. 10.2.3 Security Measures for Health Records
    71. 10.3 Current Challenges Faced by the Healthcare Workers in Covid-19 Pandemic
    72. 10.3.1 Importance and Role of Medical Records During Pandemic
    73. 10.3.2 Challenges Faced by Doctors
    74. 10.3.3 Understanding the Proposed Architecture Using COVID-19 Example
    75. 10.4 Scalable Secure Management and Access Control of Electronic Health Records at the Edge
    76. 10.4.1 The Importance of Integrating Blockchain and Edge Computing?
    77. 10.4.2 Challenges
    78. 10.5 Overview of Blockchain and Hyper Ledger Methodologies
    79. 10.5.1 Blockchain
    80. 10.5.2 Electronic Health Records (EHRs)
    81. 10.5.3 Smart Contract
    82. 10.5.4 Access Control in Medical Domain
    83. 10.5.5 Hyperledger
    84. 10.5.6 Composer Tools
    85. 10.5.7 Playground
    86. 10.5.8 Off-chain Storage
    87. 10.5.9 User Experience From Patient’s Side
    88. 10.6 Hyper Ledger-Based Proposed Architecture for Protecting Electronic Health Records
    89. 10.6.1 Proposed Architecture of the Blockchain System
    90. 10.6.2 Data Flow Diagrams
    91. 10.6.2.1 Doctors
    92. 10.6.2.2 Patient
    93. 10.6.2.3 Transaction Flow
    94. 10.7 Performance Evaluation
    95. 10.7.1 Performance of the Proposed Model
    96. 10.7.2 Performance Comparison
    97. 10.8 Conclusions and Future Caveats
    98. References
    99. Chapter 11: AI-Aided Secured ECG Live Edge Monitoring System with a Practical Use-Case
    100. 11.1 Introduction
    101. 11.1.1 Background
    102. 11.1.2 Problem Statement
    103. 11.1.3 Objective and Scope
    104. 11.2 Related Work
    105. 11.3 Proposed AI-Based System Architecture
    106. 11.3.1 Block Diagram
    107. 11.3.2 Data Collection and Pre-Processing Steps
    108. 11.3.3 Detecting Heart Abnormalities Using AI-Aided Techniques
    109. 11.4 Considered Smart ECG Monitoring System
    110. 11.4.1 Edge Hardware Components
    111. 11.4.1.1 System-on-a-Chip (SoC) Model
    112. 11.4.1.2 IoT Sensor for Heart Rate Data Acquisition
    113. 11.4.1.3 Microprocessor and Analog to Digital Converter
    114. 11.4.2 AI-Logic Component
    115. 11.4.2.1 Decision Tree
    116. 11.4.2.2 Random Forest
    117. 11.4.2.3 ANN
    118. 11.4.2.4 CNN
    119. 11.5 Bio-Authentication Application of the Considered ECG Monitoring System for Specific Use-Cases
    120. 11.6 Performance Evaluation
    121. 11.6.1 Supraventricular Arrhythmia Classification
    122. 11.6.2 Authorized User Classification for Bio-Authentication System
    123. 11.7 Challenges Involved with the Proposed System
    124. 11.8 Conclusion and Future Scope
    125. References
  13. Section III
    1. Chapter 12: Application of Unmanned Aerial Vehicles in Wireless Networks: Mobile Edge Computing and Caching
    2. 12.1 Introduction
    3. 12.1.1 Chapter Roadmap
    4. 12.2 Literature Review
    5. 12.3 Description of Caching and Mobile Edge Computing
    6. 12.3.1 Overview of Caching
    7. 12.3.1.1 Advantages
    8. 12.3.1.2 Disadvantages
    9. 12.3.2 Overview of Mobile Edge Computing
    10. 12.3.2.1 Advantages
    11. 12.3.2.2 Disadvantages
    12. 12.4 Layering of UAV-Based MEC Architecture
    13. 12.4.1 Explanation of the Layers
    14. 12.5 System Model
    15. 12.5.1 Mathematical Model of NOMA
    16. 12.5.2 Path Loss Model
    17. 12.5.3 Transmission Delay
    18. 12.5.4 Computing Model
    19. 12.5.4.1 Edge Computing Model
    20. 12.5.4.2 Local Computing Model
    21. 12.5.5 Time Consumption Model
    22. 12.5.6 Energy Consumption Model
    23. 12.6 Simulation Results
    24. 12.7 Conclusion
    25. References
    26. Chapter 13: Vehicular Edge Computing Security
    27. 13.1 Introduction
    28. 13.1.1 Chapter Roadmap
    29. 13.2 Vehicular Edge Computing Overview
    30. 13.2.1 VEC Architecture and Provided Services
    31. 13.2.2 Enabling Technologies: 5G, SDN, NFV, AI, Blockchain
    32. 13.2.3 Overview of Challenges
    33. 13.2.3.1 Task Offloading
    34. 13.2.3.2 Network Management
    35. 13.2.3.3 Caching
    36. 13.2.3.4 Data Management
    37. 13.2.3.5 Security and Privacy
    38. 13.3 Security Threats and Analysis of Potential Security Challenges
    39. 13.3.1 Access Control and Trust Management
    40. 13.3.2 Data Management
    41. 13.3.3 Decentralized Computation
    42. 13.3.4 Intrusion and Anomaly Detection
    43. 13.4 State-of-the-Art Solutions for Security Issues in VEC
    44. 13.4.1 Identity Preservation, Trust Management and Authentication
    45. 13.4.1.1 Pseudonym Management Scheme for Identity Preservation
    46. 13.4.1.2 Ensuring Trust with Distributed Reputation Management
    47. 13.4.1.3 Blockchain-Aided Cooperative Authentication
    48. 13.4.2 Blockchain-Based Secure Data Management
    49. 13.4.3 Secure Distributed Computation Techniques
    50. 13.4.4 RSU Misbehavior and Vehicle Anomaly Detection
    51. 13.5 Discussion
    52. 13.6 Conclusion
    53. References
  14. Section IV
    1. Chapter 14: On Exploiting Blockchain Technology to Approach toward Secured, Sliced, and Edge Deployed Virtual Network Functions for Improvised IoT Services
    2. 14.1 Introduction
    3. 14.2 Literature Review
    4. 14.3 Blockchain-Powered Secured Slicing
    5. 14.4 The Blockchain-Inspired Architecture for Network Slicing
    6. 14.5 The Hyperledger Fabric-Driven Prototype
    7. 14.6 Conclusion
    8. References
    9. Chapter 15: Usage of Blockchain for Edge Computing
    10. 15.1 Introduction
    11. 15.2 Applications and Benefits of Edge Computing
    12. 15.2.1 Identify the Benefits of Using Edge Computing from Different Perspectives
    13. 15.2.2 Identify the Applications of Edge Computing in Different Fields
    14. 15.3 Issues in Edge Computing
    15. 15.3.1 Issues in Security and Privacy
    16. 15.3.2 Issues in Decentralized Architecture
    17. 15.4 Integrating Blockchain in Edge Computing: The Missing Piece of the Puzzle?
    18. 15.4.1 Blockchain: Beyond Cryptocurrency
    19. 15.4.2 Advantages of Blockchain
    20. 15.4.3 How Blockchain Will Complement Edge Computing
    21. 15.4.4 How Blockchain Can be Integrated with Edge Computing
    22. 15.4.4.1 Requirements: Integrated Blockchain and Edge Computing
    23. 15.4.4.2 Overview on Existing Frameworks
    24. 15.5 Challenges and Future Scope for Incorporating Blockchain to Edge Computing
    25. 15.6 Conclusion
    26. References
  15. Index
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